کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8840580 1614691 2018 43 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Resting-State Functional Connectivity Underlying Costly Punishment: A Machine-Learning Approach
ترجمه فارسی عنوان
قابلیت اتصال به حالت استراحتی: معافیت با هزینه بالا: یک روش آموزش ماشین
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب (عمومی)
چکیده انگلیسی
A large number of studies have demonstrated costly punishment to unfair events across human societies. However, individuals exhibit a large heterogeneity in costly punishment decisions, whereas the neuropsychological substrates underlying the heterogeneity remain poorly understood. Here, we addressed this issue by applying a multivariate machine-learning approach to compare topological properties of resting-state brain networks as a potential neuromarker between individuals exhibiting different punishment propensities. A linear support vector machine classifier obtained an accuracy of 74.19% employing the features derived from resting-state brain networks to distinguish two groups of individuals with different punishment tendencies. Importantly, the most discriminative features that contributed to the classification were those regions frequently implicated in costly punishment decisions, including dorsal anterior cingulate cortex (dACC) and putamen (salience network), dorsomedial prefrontal cortex (dmPFC) and temporoparietal junction (mentalizing network), and lateral prefrontal cortex (central-executive network). These networks are previously implicated in encoding norm violation and intentions of others and integrating this information for punishment decisions. Our findings thus demonstrated that resting-state functional connectivity (RSFC) provides a promising neuromarker of social preferences, and bolster the assertion that human costly punishment behaviors emerge from interactions among multiple neural systems.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neuroscience - Volume 385, 10 August 2018, Pages 25-37
نویسندگان
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